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Visual Author Review — Severin Gylstorff

Data-driven view of publication activity, topic distribution, influence, and scientific strengths/weaknesses. Visuals first, concise interpretive text after.

Key quantitative metrics (synthesized)

• Works_count ≈13; cited_by_count ≈47; h-index = 5 (OpenAlex aggregation of public metadata).
• Most-cited work: "A Barrier to Defend" (Frontiers Immunology, 2022) — review synthesis (open access).
• Authorship mix: middle-author on large multi-author clinical/translational papers; first-author on experimental/translational papers (e.g., EVs after SIRT, IJMS 2023).

Representative evidence (open-access sources):

The Frontiers review describes pulmonary barrier models and lists Gylstorff as a middle author; it is broad (211 references) and functions as a synthesis piece useful for framing pulmonary inflammation research (Pulmonary barrier review (Frontiers, 2022)).

An original-translational first-author study in IJMS (2023) experimentally profiles extracellular vesicles after selective internal radiotherapy (SIRT) in hepatocellular carcinoma patients; this work shows the author's capability to lead translational experiments integrating clinical sampling with EV phenotyping (EV profiling after SIRT (IJMS, 2023)).

Strengths

• Translational orientation: first-author translational EV study (IJMS 2023) demonstrates experimental leadership connecting clinical samples to molecular profiling (EV study (IJMS, 2023)).
• Interdisciplinary topic mix: papers span pulmonary models, surgery/immune competence, brachytherapy biomarkers and oncology — indicates breadth across clinical and basic translational areas (Chemokine correlations after HDR brachytherapy (J Cancer Res Clin Oncol, 2022)).

Limitations & Concerns

• Modest citation impact so far (h-index = 5, ≈47 citations) — suggests early-stage career trajectory or niche translation-focused influence rather than broad paradigm-shifting work.
• Many papers are multi-author clinical/translational studies where Gylstorff is middle-author — harder to assess independent contribution from metadata alone; where he is first-author (e.g., IJMS 2023) the work is translational but limited by typical cohort sizes and single-center design.
• Top outputs include a narrative review and several translational clinical papers — strong for synthesis and applied research but fewer standalone mechanistic, high-signal basic science breakthroughs at present.

Concrete suggestions to raise impact

1. Publish larger, multicenter translational cohorts or combine EV profiling with functional assays to increase effect-size credibility and citation reach.
2. Lead mechanistic follow-ups (in vitro/in vivo) to translational findings to move from correlative biomarkers to causal mechanisms.
3. Increase first-author mechanistic papers and data-sharing (raw EV datasets, scripts) to improve reproducibility and downstream reuse.

Other representative publications (open access):

• TNF-α as liver injury biomarker (In Vivo, 2022)
• 3D-cell phantom electroporation (Sci Rep, 2024)

What would change this assessment?

• Large, well-powered multicenter studies or independent replication of translational biomarkers with increased citation uptake would raise the scientific score substantially.
• New first-author mechanistic discoveries (causal EV biology, validated biomarkers predicting clinical outcomes) would move the profile from translational-to-high-impact basic/translational science.